Biology subjects

Mohiyuddin, M.

Publications and source records attributed to Mohiyuddin, M..

5 recordsLinked to original sources

bioRxiv2025

Functional Role of Small Extrachromosomal Circular DNA in Colorectal Cancer

Extrachromosomal circular DNA (eccDNA) are circular DNA molecules that originate from chromosomal DNA but exist independently. While large eccDNA (ecDNA) contributes to tumorigenesis, the role of smaller eccDNA (<100,000 base pairs) in cancer remains unclear. Our analysis of 25 colorectal cancer (CRC) tumors and adjacent non-tumorous tissues revealed that eccDNA is significantly more abundant in tumor tissues, correlating strongly with chromosomal amplifications. The presence of whole intact genes on 1.29% of eccDNA was non-random. We identified 84 genes that recurred across tumors of multiple patients when present on eccDNA with 19% of genes being cancer-associated. eccDNA-borne genes were often accompanied by increased expression, and their contribution to expression was much larger than that from linear amplifications and the larger ecDNA. The cytokine gene CXCL5 exemplified this phenomenon, showing substantial copy-number increase and upregulation when present on eccDNA. Functional validation in cell lines showed that CXCL5 eccDNA enhanced transcriptional output and immune cell recruitment function. The recurrence and overexpression of CRC-related genes on eccDNA indicate their selection in tumors, suggest eccDNA can serve as a novel mechanism for dynamically influencing gene expression and is capable of conferring cancer phenotypes to cells. Analysis of chromatin landscapes revealed that eccDNA preferentially forms at sites of open chromatin and active transcription, with architectural boundaries marked by CTCF protein. Clinically, patients with higher eccDNA levels showed poorer relapse-free survival. These findings suggest that circular DNA elements across the entire size spectrum participate in cancer evolution, positioning eccDNA as a potential therapeutic target and prognostic biomarker.

cancer biology↗

bioRxiv2019

Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing Data

Accurate detection of somatic mutations is challenging but critical to the understanding of cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network based somatic mutation detection approach and demonstrated performance advantages on in silico data. In this study, we used the first comprehensive and well-characterized somatic reference samples from the SEQC-II consortium to investigate best practices for utilizing deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for these reference samples by the consortium, we identified strategies for building robust models on multiple datasets derived from samples representing real scenarios. The proposed strategies achieved high robustness across multiple sequencing technologies such as WGS, WES, AmpliSeq target sequencing for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages (ranging from 10x - 2000x). NeuSomatic significantly outperformed conventional detection approaches in general, as well as in challenging situations such as low coverage, low mutation frequency, DNA damage, and difficult genomic regions.

genomics↗

bioRxiv2019

Establishing reference samples for detection of somatic mutations and germline variants with NGS technologies

We characterized two reference samples for NGS technologies: a human triple-negative breast cancer cell line and a matched normal cell line. Leveraging several whole-genome sequencing (WGS) platforms, multiple sequencing replicates, and orthogonal mutation detection bioinformatics pipelines, we minimized the potential biases from sequencing technologies, assays, and informatics. Thus, our \"truth sets\" were defined using evidence from 21 repeats of WGS runs with coverages ranging from 50X to 100X (a total of 140 billion reads). These \"truth sets\" present many relevant variants/mutations including 193 COSMIC mutations and 9,016 germline variants from the ClinVar database, nonsense mutations in BRCA1/2 and missense mutations in TP53 and FGFR1. Independent validation in three orthogonal experiments demonstrated a successful stress test of the truth set. We expect these reference materials and \"truth sets\" to facilitate assay development, qualification, validation, and proficiency testing. In addition, our methods can be extended to establish new fully characterized reference samples for the community.

bioinformatics↗

bioRxiv2019

ecTMB: a robust method to estimate and classify tumor mutational burden

Tumor Mutational Burden (TMB) is a measure of the abundance of somatic mutations in a tumor, which has been shown to be an emerging biomarker for both anti-PD-(L)1 treatment and prognosis. Nevertheless, multiple challenges still hinder the adoption of TMB for clinical decision-making. The key challenges are the inconsistency of TMB measurement among assays and a lack of meaningful threshold for TMB classification. We describe a powerful and flexible statistical framework for estimation and classification of TMB (ecTMB). ecTMB uses an explicit background mutation model to predict TMB robustly and consistently. In addition to the two known TMB subtypes, TMB-high and TMB-low, we discovered a novel TMB subtype, named TMB-extreme, which was significantly associated with patient survival outcome. This discovery enabled ecTMB to classify samples to biologically and clinically relevant subtypes defined by TMB.

bioinformatics↗

bioRxiv2018

Deep convolutional neural networks for accurate somatic variant calling

We present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.

genomics↗